The Implementation of Support Vector Machine (SVM) using FPGA for Human Detection

Madadum H., BECERİKLİ Y.

10th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Türkiye, 30 Kasım - 02 Aralık 2017, ss.1286-1290 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1286-1290
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Human or pedestrian detection is an attractive headline and has been proposed in computer vision and machine learning fields. Real time detection and low power system is a critical challenges. Support Vector Machine algorithm with Histograms of oriented gradients (HOG) feature descriptor is given a high successful result, fast and reliable, for human detection. Therefore, this paper demonstrates how to implement HOG feature descriptor with Support Vector Machine (SVM) using FPGA and presents a report that includes FPGA's resource utilization, time consuming, power consumption and SVM accuracy results.